import numpy as np
from sklearn import preprocessing, cross_validation, neighbors, svm
import pandas as pd

df = pd.read_csv('breast-cancer-wisconsin.data.txt')
df.replace('?', -9999, inplace=True)
df.drop('id', 1, inplace=True)

x = np.array(df.drop(['class'], 1))
y = np.array(df['class'])
x_train, x_test, y_train, y_test = cross_validation.train_test_split(x, y, test_size=0.2)

# clf = neighbors.KNeighborsClassifier()
clf = svm.SVC()
clf.fit(x_train, y_train)

accuracy = clf.score(x_test, y_test)
print(accuracy)

example_measures = np.array([[4, 2, 1, 1, 1, 2, 3, 2, 1], [4, 2, 1, 2, 2, 2, 3, 2, 1]])

example_measures = example_measures.reshape(len(example_measures), -1)

prediction = clf.predict(example_measures)
print(prediction)
